Prediction Of Protein Content Research Articles

Comparative analysis of modified partial least squares regression and hybrid deep learning models for predicting protein content in Perilla (Perilla frutescens L.) seed meal using NIR spectroscopy

Perilla seed meal (PSM), a byproduct of oil extraction from Perilla frutescens L. seeds, is rich in protein (24.26–42.85%) and holds potential as an economical and sustainable animal feed. Traditional methods for assessing protein content are labor-intensive and costly. This study explores Near-Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise, and non-destructive determination of PSM protein content in 126 samples. We developed and evaluated Modified Partial Least Squares (MPLS) regression and deep learning (DL) models, including 1D-CNN (Convolutional Neural Network), LSTM Long Short-Term Memory), and hybrid architectures incorporating skip connections, inception modules, and spectral derivatives. Model performance was validated externally using parameters such as RSQexternal (R-squared), bias, SEP(C) (Standard Error of Prediction), RPD (Residual Prediction Deviation), slope, SD (Standard Deviation), p-value (≥0.05), and the correlation between reference and predicted values. The 1D CNN-LSTM-Inception derivative 1 model achieved the best performance (RPD: 8.0, RSQexternal: 0.98), followed by the MPLS-based model (RPD: 4.88, RSQexternal: 0.96) and the 1D CNN derivative 1 model (RPD: 3.07, RSQexternal: 0.96). These models provide a reliable and advanced technology for the non-destructive screening of PSM protein content, thus aiding in the rapid identification and selection of superior perilla chemotypes from varied backgrounds.

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging.

Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed's nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.

Spectroscopy‐based prediction of 73 wheat quality parameters and insights for practical applications

AbstractBackground and ObjectivesQuality assessment of bread wheat is time‐consuming and requires the determination of many complex characteristics. Because of its simplicity, protein content prediction using near‐infrared spectroscopy (NIRS) serves as the primary quality attribute in wheat trade. To enable the prediction of more complex traits, information from Raman and fluorescence spectra is added to the NIR spectra of whole grain and extracted flour. Model robustness is assessed by predictions across cultivars, locations, and years. The prediction error is corrected for the measurement error of the reference methods.FindingsSuccessful prediction, robustness testing, and measurement error correction were achieved for several parameters. Predicting loaf volume yielded a corrected prediction error RMSECV of 27.5 mL/100 g flour and an R² of 0.86. However, model robustness was limited due to data distribution, environmental factors, and temporal influences.ConclusionsThe proposed method was proven to be suitable for applications in the wheat value chain. Furthermore, the study provides valuable insights for practical implementations.Significance and NoveltyWith up to 1200 wheat samples, this is the largest study on predicting complex characteristics comprising agronomic traits; dough rheological parameters measured by Extensograph, micro‐doughLAB, and GlutoPeak; baking trial parameters like loaf volume; and specific ingredients, such as grain protein content, sugars, and minerals.

Use of animal origin protein concentrates in bread baking

Protein deficiency in the human diet is a widespread problem that affects all body systems. Nutrition adjustment appears to be one of the most effective ways to prevent this problem. This study was aimed at investigating the possibilities of using animal origin protein concentrates in bread baking industry.
 Study objects included five breads: one control and four samples containing protein concentrates. The test wheat bread samples contained 7, 9, and 11% of milk protein concentrate and 7% of whey protein concentrate to the mass of flour in the dough. Rheological parameters of the dough were obtained using an alveograph and a farinograph. The specific volume and sensory characteristics of the baked products as well as crumb deformation were evaluated. The nutritional value of two samples – control and with 9% of milk protein concentrate addition – was determined by calculation.
 Whey protein concentrate had a negative influence on the dough rheology. Low water binding capacity and specific volume as well as hard crumb make the usage of this product in bread baking unacceptable. The palatability test showed that bread supplemented with 9% of milk protein concentrate had the best sensory characteristics, compared to control and other test samples (with 7 and 11% of this concentrate). The predicted protein content was equal to 11.6 g/100 g that is 35% higher than in the control sample. Consequently, milk protein concentrate has a potential to improve biological value of the bread that can be further helpful to prevent the protein deficiency.
 Milk protein concentrate can be successfully used in the bread making process as an ingredient to correct the nutritional and biological value of baked products.

Spatial heterogeneity analysis of silique chlorophyll a fluorescence-based photosynthetic traits for rapeseed yield and quality assessment

Photosynthesis is an important process that contributes to capturing light energy and transforming it into chemical energy in plants. To date, most studies on the photosynthetic traits focus on plant leaves, while the photosynthetic response of non-foliar organs such as siliques of oilseed rapes at the vertical distribution of the canopy, and their contribution to yield and quality traits remain poorly understood. Therefore, this study aims to elucidate the spatial heterogeneity of silique chlorophyll a fluorescence features and estimate targeted rapeseed yield and quality traits in a non-destructive manner before harvest. Field experiments were conducted for silique chlorophyll a fluorescence, morphological traits, yield and quality traits measurements within different spatial layers under varied nitrogen treatment levels. Correlation analysis and random forest (RF) algorithm were applied to identify crucial JIP-test parameters in different spatial layers and to evaluate their performance on rapeseed yield and quality evaluation. Our results found that several JIP-test parameters (ABS/RC, TRo/RC, and ETo/RC) of the siliques at the top layer related to the activity of reaction center (RC) presented superior photoprotection and photosynthetic capacities compared to those in the middle and bottom layers, indicating a significant spatial heterogeneity of the primary photosynthesis of siliques. As for yield and quality traits, rapeseeds had the highest oil content at the bottom layer followed by the middle and top layers, whereas protein content and yield traits were completely opposite to this tendency. RF model further confirmed that the JIP-test parameters acquired from siliques of the upper layers were more sensitive to the yield and protein content prediction, while the oil content can be better estimated using the JIP-test parameters of siliques at the bottom layer. Furthermore, a relatively weak relationship was identified between the glucosinolates and erucic acid content and JIP-test parameters. We expect that findings in this study could provide a new way to understand the spatial heterogeneity of silique photosynthetic traits, which might be useful for developing high yield and quality cultivars in rapeseed breeding.

Wheat Yield and Protein Estimation with Handheld and Unmanned Aerial Vehicle-Mounted Sensors

Accurate sensor-based prediction of crop yield and grain quality in-season would enable growers to adjust nitrogen (N) fertilizer management for optimized production. This study assessed the feasibility (and compared the accuracy) of wheat (Triticum aestivum L.) yield, grain N uptake, and protein content prediction with in-season crop spectral reflectance measurements (Normalized Difference Vegetative Index, NDVI) obtained with a handheld GreenSeeker (GS) sensor and an Unmanned Aerial Vehicle (UAV)-mounted sensor. A strong positive correlation was observed between GS NDVI and UAV NDVI at Feekes 5 (R2 = 0.78) and Feekes 10 (R2 = 0.70). At Feekes 5, GS NDVI and UAV NDVI explained 42% and 43% of wheat yield, respectively. The correlation was weaker at Feekes 10 (R2 of 0.34 and 0.25 for GS NDVI and UAV NDVI, respectively). The accuracy of wheat grain N uptake prediction was comparable to that of yield: the R2 values for GS NDVI and UAV NDVI were 0.53 and 0.37 at Feekes 5 and 0.13 and 0.20 at Feekes 10. We found that neither GS NDVI nor UAV NDVI in-season data were useful in prediction of wheat grain protein content. In conclusion, wheat yield and grain N uptake can be estimated at Feekes 5 using either handheld or aerial based NDVI with comparable accuracy.

Prediction of Protein Content in Pea (Pisum sativum L.) Seeds Using Artificial Neural Networks

Pea (Pisum sativum L.) is a legume valued mainly for its high seed protein content. The protein content of pea is characterized by a high lysine content and low allergenicity. This has made consumers appreciate peas increasingly in recent years, not only for their taste, but also for their nutritional value. An important element of pea cultivation is the ability to predict protein content, even before harvest. The aim of this research was to develop a linear and a non-linear model for predicting the percentage of protein content in pea seeds and to perform a comparative analysis of the effectiveness of these models. The analysis also focused on identifying the variables with the greatest impact on protein content. The research included the method of machine learning (artificial neural networks) and multiple linear regression (MLR). The input parameters of the models were weather, agronomic and phytophenological data from 2016–2020. The predictive properties of the models were verified using six ex-post forecast measures. The neural model (N1) outperformed the multiple regression (RS) model. The N1 model had an RMS error magnitude of 0.838, while the RS model obtained an average error value of 2.696. The MAPE error for the N1 and RS models was 2.721 and 8.852, respectively. The sensitivity analysis performed for the best neural network showed that the independent variables most influencing the protein content of pea seeds were the soil abundance of magnesium, potassium and phosphorus. The results presented in this work can be useful for the study of pea crop management. In addition, they can help preserve the country’s protein security.

Non-destructive prediction of protein contents of soybean seeds using near-infrared hyperspectral imaging

Protein content is one of the most crucial factors in soybean quality. However, the breeding procedure necessitates the time-consuming and costly selection of elite genotypes from many experimental lines in a destructive manner. The present work aims to predict protein content in single soybean seeds non-destructively using Near-Infrared (NIR) Hyperspectral Imaging (HSI). 1491 seed samples from 3 varieties of the low, medium, and high protein content (consisting of 371, 560, and 560 samples, respectively) were measured using the NIR-HSI system with a range of 900–1800 nm. The spectral data extracted from the HSI 3D hypercube were synchronised to the reference values examined from chemical analysis. The calibration model was constructed using partial least square regression (PLSR) methods based on the 70 % spectral data and then validated using the remaining 30 % of data. The result showed that the NIR-HSI technique is a promising method to predict protein content in soybean seeds, as shown by an R2 of 0.92 and a root mean square error (RMSE) of 1.08 %. In addition, the chemical images visualised the distribution of protein content for the multiple soybean seed showed the possibility of the developed technique for the use of rapid evaluation of massive samples in the processing line.

Predicting the performance of handheld near-infrared photonic sensors from a master benchtop device

Many industries see a shifting focus towards performing on-site analysis using handheld spectroscopic devices. A determining factor for decision-making on the commissioning of these devices is available information on the potential performance of the device for specific applications. By now, myriad handheld solutions with very different specifications and pricing are available on the market. Although specifications are generally available for new devices, this does not directly quantify or predict how available devices will perform for targeted cases. We present a novel chemometric method to estimate the prediction performance of handheld NIR hardware and apply it to estimate the performance of two commercially available handheld NIR technologies in predicting protein content (ranging 120–180 g kg−1) in pig feed from existing data of a benchtop device. Adjusting benchtop data to the wavelength range and resolution of the handheld device lead to over-optimistic estimates of the handheld performances. Our method additionally utilizes information on the error structure of the handheld devices for the estimation. It yielded performance estimates differing less than 1 g kg−1 from the experimentally determined handheld performances and similar model parameters. Our method was effective for linear and nonlinear calibration algorithms, also when estimating performance after averaging multiple scans. Replicate spectra of twenty samples recorded using the handheld were required for replication error estimation to obtain an accurate performance estimation. The error structure could be reported by manufacturers in the future for this approach to be universally employed for predictive quantitative technology assessment. Overall, our method provides estimates of the performance of a handheld device for a specific task with minimal testing required and can thus be used as a device or application screening tool before committing to develop calibrations.

Physicochemical mechanisms of FT-NIRS age prediction in fish otoliths

Context Fourier transform near-infrared spectroscopy (FT-NIRS) is of interest to fisheries managers for rapid age prediction in fish otoliths, yet the underlying prediction mechanism is unknown. Aims To better understand drivers of FT-NIRS age prediction, we evaluated FT-NIRS spectra and age prediction models for otoliths of red snapper, Lutjanus campechanus, related to otolith structure, mass, and constituents (calcium carbonate (CaCO3) and protein). Methods Spectra were collected from a set of whole otoliths (n = 84, 0–28 years) and again sequentially after grinding to powder and subsampling a fixed mass of each ground otolith. Protein content was also measured (n = 26) and related to spectra. Key results Age prediction was diminished in ground and fixed-mass otolith models, but remained within 2 years of traditional ages. Protein content (0.43–0.92% weight) increased significantly with age, implying a concomitant decrease in CaCO3 content. FT-NIRS models predicted protein content to within 0.04%, but protein variability hindered modelling. Spectral characteristics of both CaCO3 and protein are evident in otolith spectra and are implicated in age-prediction models. Conclusions Changes in otolith composition, mass, and structure underlie FT-NIRS age prediction, but compositional changes inform the majority of age prediction. Implications These results provide a foundation for understanding FT-NIRS age prediction.

Predicting protein content of silage maize using remotely sensed multispectral imagery and proximal leaf sensing

Abstract Timely estimation of silage maize protein provides an effective decision to adapt optimized strategies for nitrogen fertilizer management and also harvesting time for farmers. So, this research aimed to investigate whether using vegetative indices (VIs) derived from UAV remotely sensed multispectral (with 520–900 nm wavelengths) imagery and also Soil Plant Analysis Development (SPAD) greenness index can be used to detect the leaf protein concentration (LPC) of silage maize, as a function of various nitrogen rates (0, 50, 100, and 150% of recommended dosage). Results of principal component analysis (PCA) suggested that LPC was highly correlated with leaf greenness index in all developmental stages. In addition, LPC was highly correlated with most of the VIs investigated. A PCA clustering showed the meaningful pattern of N rates. Higher LPC values, VIs, and greenness index were more concentrated in the higher nitrogen (N100% and N150%) sectors. Nitrogen Reflectance Index (NRI) was identified as the most important VIs to monitor and predict LPC in the silage maize field, showing a strong polynomial relationship with LPC in both eight-leaf collar (V8) (R2 = 0.81, p ≤ 0.01) and tasseling (VT) (R2 = 0.98, p ≤ 0.001) stages. In addition, among VIs, the Normalized Difference Vegetation Index (NDVI) demonstrated a significant linear regression relationship with LPC (R2 = 0.80, p ≤ 0.01) in the VT. Findings suggested the high potential of VIs extracted by UAV-taken multispectral imagery and also SPAD proximal sensing to help farmers rapidly diagnose LPC in silage maize, in line with the objectives of precision farming.

Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations

BackgroundPeatlands are expected to experience sustained yet fluctuating higher temperatures due to climate change, leading to increased microbial activity and greenhouse gas emissions. Despite mounting evidence for viral contributions to these processes in peatlands underlain with permafrost, little is known about viruses in other peatlands. More generally, soil viral biogeography and its potential drivers are poorly understood at both local and global scales. Here, 87 metagenomes and five viral size-fraction metagenomes (viromes) from a boreal peatland in northern Minnesota (the SPRUCE whole-ecosystem warming experiment and surrounding bog) were analyzed for dsDNA viral community ecological patterns, and the recovered viral populations (vOTUs) were compared with our curated PIGEON database of 266,125 vOTUs from diverse ecosystems.ResultsWithin the SPRUCE experiment, viral community composition was significantly correlated with peat depth, water content, and carbon chemistry, including CH4 and CO2 concentrations, but not with temperature during the first 2 years of warming treatments. Peat vOTUs with aquatic-like signatures (shared predicted protein content with marine and/or freshwater vOTUs) were significantly enriched in more waterlogged surface peat depths. Predicted host ranges for SPRUCE vOTUs were relatively narrow, generally within a single bacterial genus. Of the 4326 SPRUCE vOTUs, 164 were previously detected in other soils, mostly peatlands. None of the previously identified 202,371 marine and freshwater vOTUs in our PIGEON database were detected in SPRUCE peat, but 0.4% of 80,714 viral clusters (VCs, grouped by predicted protein content) were shared between soil and aquatic environments. On a per-sample basis, vOTU recovery was 32 times higher from viromes compared with total metagenomes.ConclusionsResults suggest strong viral “species” boundaries between terrestrial and aquatic ecosystems and to some extent between peat and other soils, with differences less pronounced at higher taxonomic levels. The significant enrichment of aquatic-like vOTUs in more waterlogged peat suggests that viruses may also exhibit niche partitioning on more local scales. These patterns are presumably driven in part by host ecology, consistent with the predicted narrow host ranges. Although more samples and increased sequencing depth improved vOTU recovery from total metagenomes, the substantially higher per-sample vOTU recovery after viral particle enrichment highlights the utility of soil viromics.3EUUqCyiCvMAAvxFuvvQVmVideo abstract The importance of Minnesota peat viromes in revealing terrestrial and aquatic niche partitioning for viral populations

Comparative Assessment of Various Methods for Predicting Protein Content in Wheat Grain

Comparative Assessment of Various Methods for Predicting Protein Content in Wheat Grain

Comparisons of sensors to predict spring wheat grain yield and protein content

AbstractCanopy vegetation indices can predict the spatial variability in grain yield before harvest across large spatial scale. Spring wheat (Triticum aestivum L.) canopy reflectance data using a hand‐held active sensor (RapidScan CS‐45) and a small unmanned aerial vehicle (sUAV)‐based passive light optical sensor (Micasense RedEdge) were collected three times during flag leaf to heading stages (in the month of June and July) from 16 site‐years across the Red River Valley of Minnesota and North Dakota. Potential of vegetation indices (VIs), red normalized vegetation (RNDVI) and red edge normalized vegetation (RENDVI), were compared to predict grain yield and protein content. Linear regression between two sensors showed a significant (p < .05) relationship based on RNDVI (R2 = .69) and RENDVI (R2 = .55) values (n = 48). Only for the 2nd year, RENDVI from both sensors could predict the grain yield; but only the RENDVI from sUAV‐based passive sensor could predict the protein content at Haun stages between five and eight (R2 = .60, RMSE = 14.8, p = .02). Use of sUAV based passive sensor has potential to predict protein content but selecting optimal growth stage and validation of developed regression models in neighboring fields are critical for the accurate prediction.

Qualitative and Quantitative Analysis of Sorghum Grain Composition Including Protein and Tannins Using ATR-FTIR Spectroscopy

Chemical composition and grain quality traits are important factors related to the end-use quality of sorghum. Thus, rapid and non-destructive analytical methods for screening sorghum for desirable grain quality traits benefit the industries using sorghum for food, feed, and biofuels as well as sorghum breeders and seed companies working to improve the grain quality of sorghum. In the present study, a method based on Fourier-transform infrared spectroscopy (FTIR) was developed to characterize the major chemical components in sorghum flour. Sorghum flour had three major FTIR active regions from 1800 to 800 cm−1. As expected, FTIR spectra revealed that starch was the primary compound in sorghum flour, followed by protein with only minor amounts of lipids and phenolic acids. Phenolic acids were characterized by a peak at 1709 cm−1, which varied in intensity across sorghum varieties. Tannin sorghum could be discriminated from non-tannin sorghum by a shift in O–H stretch band around 3300 cm−1 and their C=C absorption peaks at 1608 and 1522 cm−1. Flour from the outer part of the kernel (corneous endosperm) had greater protein and lipid absorbance while the inner kernel (floury endosperm) had stronger starch absorbance. Pearson’s correlation analyses between FTIR peak intensities and protein/starch content showed that starch content did not have a significant correlation with infrared peaks, whereas the amide I peak at 1652 cm−1 (C=O stretching group), highly correlated with protein content (P ≤ 0.05). A calibration curve (Y = 127.79X-14.345, R2 = 0.9454) was built using peak height at 1652 cm−1 to predict protein content and this was applied to determining protein digestibility.

Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches

ABSTRACT Prediction of crop yield and quality is an essential component of successful implementation of precision agriculture. Given the recent commercialization of low-cost multispectral cameras mounted on unmanned aerial vehicles and advances in machine learning techniques, prediction systems for crop characteristics can be more precisely developed using machine learning techniques. Therefore, the model performances for predicting wheat grain yield and protein content between the machine learning algorithms based on spectral reflectance and plant height (e.g. random forest and artificial neural network) and the traditional linear regression based on vegetation indices were compared. Although the machine learning approaches based on reflectance could not improve the grain yield prediction accuracy, they have great potential for development in predicting protein content. The linear regression model based on a 2-band enhanced vegetation index was capable of predicting the yield with a root-mean-square error (RMSE) of 972 kg ha−1. The random forest model based on reflectance was capable of predicting the protein content with an RMSE of 1.07%. The reflectance may have been linearly correlated with total biomass; thus, it was also linearly correlated with grain yield. There was a nonlinear relationship between the grain yield and protein content, which may have resulted in the higher model performance of the machine learning approaches in predicting protein content. However, this relationship would be variable according to the environment and agronomic practice. Further, field-scale research is required to assess how this relationship can be varied and affect the model generality, particularly when predicting protein content.

Extended multiplicative signal correction to improve prediction accuracy of protein content in weathered sorghum grain samples

AbstractBackground and objectivesSorghum grains may be subjected to weathering damage in the field as naked grains on the panicle are exposed to adverse environmental conditions during grain development or dry down before harvest. A near infrared (NIR) protein calibration model did not work as expected for weathered grains. Extended multiplicative signal correction was tested as a preprocessing method for spectra to improve the prediction accuracy of the model when weathered grain samples are evaluated.FindingsNIR spectra of weathered sorghum grain samples showed marked distortions with a higher apparent absorbance at lower wavelengths in the 950–1650 nm spectral range. A partial least squares calibration developed with sound grain samples using spectra preprocessed with multiplicative signal correction (MSC) predicted protein content of validation sample set of sound grains with a root mean square error of prediction (RMSEP) = 0.67% and coefficient of determination (r2) = 0.83. However, performance of this calibration model dropped with RMSEP = 13.14% and r2 = 0.04 when used to predict proteins in weathered grain samples. Introduction of weathered grain spectra to the calibration improved predictive performance with RMSEP = 0.92% and r2 = 0.69. Application of extended multiplicative scatter correction (EMSC) to preprocess the spectra of the calibration with weathered grain samples enhanced the prediction performance with RMSEP = 0.61% and r2 = 0.85.ConclusionsInclusion of weathered grain spectra in the calibration and preprocessing spectra with EMSC markedly improved the robustness of the sorghum protein calibration.Significance and noveltySorghum breeders using NIR spectroscopy for evaluation of protein levels in sorghum breeding lines can get the weathered grain samples also analyzed for protein levels by using this method.

FTIR spectroscopy for quality evaluation of sports supplements on the Polish market

Introduction. Our study aimed to apply medium infrared (MIR/FTIR) spectroscopy to evaluate the quality of various sports supplements available in the Polish shops and gyms. Study objects and methods. The study objects included forty-eight sports supplements: whey (15 samples), branched-chain amino acids (12 samples), creatine (3 samples), mass gainers (6 samples), and pre-workouts (12 samples). First, we determined the protein quantity in individual whey supplements by the Kjeldahl method and then correlated the results with the measured FTIR spectra by chemometric methods. The principal component analysis (PCA) was used to distinguish the samples based on the measured spectra. The samples were grouped according to their chemical composition. Further, we correlated the spectra with the protein contents using the partial least squares (PLS) regression method and mathematic transformations of the FTIR spectral data. Results and discussion. The analysis of the regression models confirmed that we could use FTIR spectra to estimate the content of proteins in protein supplements. The best result was obtained in a spectrum region between 1160 and 2205 cm–1 and after the standard normal variate normalization. R2 for the calibration and validation models reached 0.85 and 0.76, respectively, meaning that the models had a good capability to predict protein content in whey supplements. The RMSE for the calibration and validation models was low (2.7% and 3.7%, respectively). Conclusion. Finally, we proved that the FTIR spectra applied together with the chemometric analysis could be used to quickly evaluate the studied products.

Assessment of High Temperature Effects on Grain Yield and Composition in Bread Wheat Commercial Varieties

Wheat is one of the most important cereals for food and feed, and it is, therefore, necessary to determine the effects of short-term high temperature events (heatwaves) during grain filling. These heatwave events are increasingly common, especially in Portugal. In this work, seven commercial varieties recommended for production in Portugal were submitted to one-week high temperature (HT) treatment ten days after anthesis to evaluate heat effects on grain yield and quality. Grain yield parameters, such as grain number and weight, were evaluated as well as grain composition through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. Variation in HT response between varieties was detected. Grain number and weight tended to decrease in most varieties analyzed. However, two varieties proved to be more resilient since grain number and weight remain unaltered in the Bancal variety, which is the one with better yield results, and even increased in the Pata Negra variety. Regarding grain composition, the comparison between ATR-FTIR spectra of milled grains from control and HT plants revealed alterations in peaks assigned to polysaccharides and proteins. Additionally, a model was built based on nitrogen elemental analysis to predict protein content in flour samples through spectral data that corroborated the differences identified by spectra profile comparison. Moreover, both analyses showed that the intervarietal diversity observed in control conditions was significantly reduced in HT treated plants. The results obtained highlight the intervarietal diversity of wheat response to HT, regarding grain yield parameters, grain composition, and particularly, protein content.

Response Surface Approach for Optimization of Protein Hydrolysis from Reutealis trisperma Cake as Potential Animal Feedstock

The conditions for protein hydrolysis were optimized to prepare Reutealis trisperma cake for potential animal feedstock. The cake’s content was 34.03 % protein, 6.32 % moisture, 18.56 % total sugar, 15.58 % lipid and 25.51% others. Other components in cake could be fibre and lignin. The cake is a byproduct of mechanical pressing process of the seeds and contains high protein content (34.03%). It was ground prior the hydrolysis process. A central composite design including concentration of NaOH, ratio of cake to NaOH, time and temperature were used to develop second order model to predict protein content under various experimental conditions. Protein yield was primarily affected by ratio pressed cake to NaOH and concentration of NaOH. Based on the Response Surface Methodology (RSM) model, maximum yield of protein was 11.33% which was obtained at cake/solvent ratio 1: 50; 1.5 % w/v NaOH; 15 minutes of hydrolysis at 40oC. The actual maximum protein yield from the experiment was obtained at cake/solvent ratio 1: 40; 1.5 % w/v NaOH; 20 minutes of hydrolysis at 45oC which was 21.28 %.Keywords: animal feedstock; protein hydrolysis; response surface; Reutealis trisperma